NatComm2020/In-vivo/Calibration and longitudinal assessment/Day1-2/Calibration scripts/CalibrationRec2_ExportDatFile.py

# -*- coding: utf-8 -*-
"""
Created on Sat Oct 13 23:47:41 2018

@author: aemdlabs
"""

# -*- coding: utf-8 -*-
"""
Created on Thu Sep 27 08:36:30 2018

@author: aemdlabs
"""

from PhyREC.NeoInterface import NeoSegment#, ReadMCSFile
import PhyREC.PlotWaves as Rplt
import quantities as pq
import matplotlib.pyplot as plt
import numpy as np
import neo
import PhyREC.SignalProcess as RPro
import deepdish as dd

def ReadMCSFile(McsFile, OutSeg=None, SigNamePrefix=''):
    import McsPy.McsData as McsData

    Dat = McsData.RawData(McsFile)
    Rec = Dat.recordings[0]
    NSamps = Rec.duration

    if OutSeg is None:
        OutSeg = NeoSegment()

    for AnaStrn, AnaStr in Rec.analog_streams.iteritems():
        if len(AnaStr.channel_infos) == 1:
            continue

        for Chn, Chinfo in AnaStr.channel_infos.iteritems():
            print ('Analog Stream ', Chinfo.label, Chinfo.sampling_frequency)
            ChName = str(SigNamePrefix + Chinfo.label)
            print(ChName)

            Fs = Chinfo.sampling_frequency
            Var, Unit = AnaStr.get_channel_in_range(Chn, 0, NSamps)
            sig = neo.AnalogSignal(pq.Quantity(Var, Chinfo.info['Unit']),
                                   t_start=0*pq.s,
                                   sampling_rate=Fs.magnitude*pq.Hz,
                                   name=ChName)

            OutSeg.AddSignal(sig)
    return OutSeg

    



MCSMapI={'SE1':'Ch03',
         'SE2':'Ch05',
         'SE3':'Ch01',
         'SE4':'Ch02',
         'SE5':'Ch22',
         'SE6':'Ch06',
         'SE7':'Ch16',
         'SE8':'Ch37',
         'SE9':'Ch20',
         'SE10':'Ch10',
         'SE11':'Ch24',
         'SE12':'Ch08',
         'SE13':'Ch14',
         'SE14':'Ch04',
         'SE15':'Ch18',
         'SE16':'Ch33',
         'SE17':'Ch34',
         'SE18':'Ch60',
         'SE19':'Ch38',
         'SE20':'Ch64',
         'SE21':'Ch40',
         'SE22':'Ch56',
         'SE23':'Ch42',
         'SE24':'Ch70',
         'SE25':'Ch66',
         'SE26':'Ch65',
         'SE27':'Ch68',
         'SE28':'Ch67',
         'SE29':'Ch55',
         'SE30':'Ch62',
         'SE31':'Ch58',
         'SE32':'Ch69',
         'ME1':'Ch57',
         'ME2':'Ch61',
         'ME3':'Ch53',
         'ME4':'Ch63',
         'ME5':'Ch52',
         'ME6':'Ch41',
         'ME7':'Ch49',
         'ME8':'Ch51',
         'ME9':'Ch46',
         'ME10':'Ch45',
         'ME11':'Ch44',
         'ME12':'Ch39',
         'ME13':'Ch54',
         'ME14':'Ch43',
         'ME15':'Ch50',
         'ME16':'Ch47',
         'ME17':'Ch32',
         'ME18':'Ch27',
         'ME19':'Ch30',
         'ME20':'Ch29',
         'ME21':'Ch28',
         'ME22':'Ch25',
         'ME23':'Ch26',
         'ME24':'Ch07',
         'ME25':'Ch21',
         'ME26':'Ch11',
         'ME27':'Ch17',
         'ME28':'Ch15',
         'ME29':'Ch13',
         'ME30':'Ch31',
         'ME31':'Ch19',
         'ME32':'Ch09',
         'ME0':'Ch12',
         'SE33':'Ch59'}

MCSMap = {v: k for k, v in MCSMapI.iteritems()}       
                 
#Col, Row  
MCSMapFacingDown={'Ch58':(0,1),
                  'Ch57':(0,2),
                  'Ch56':(0,3),
                  'Ch55':(0,4),
                  'Ch54':(0,5),
                  'Ch53':(0,6),
                  'Ch52':(0,7),
                  'Ch51':(0,8),
                  'Ch50':(0,9),
                  'Ch49':(0,10),
                  'Ch60':(1,0),
                  'Ch61':(1,1),
                  'Ch62':(1,2),
                  'Ch63':(1,3),
                  'Ch64':(1,4),
                  'Ch65':(1,5),
                  'Ch43':(1,6),
                  'Ch44':(1,7),
                  'Ch45':(1,8),
                  'Ch46':(1,9),
                  'Ch47':(1,10),
                  'Ch70':(2,0),
                  'Ch69':(2,1),
                  'Ch68':(2,2),
                  'Ch67':(2,3),
                  'Ch66':(2,4),
                  'Ch42':(2,5),
                  'Ch41':(2,6),
                  'Ch40':(2,7),
                  'Ch39':(2,8),
                  'Ch38':(2,9),
                  'Ch37':(2,10),
                  'Ch01':(3,0),
                  'Ch02':(3,1),
                  'Ch03':(3,2),
                  'Ch04':(3,3),
                  'Ch05':(3,4),
                  'Ch06':(3,5),
                  'Ch30':(3,6),
                  'Ch31':(3,7),
                  'Ch32':(3,8),
                  'Ch33':(3,9),
                  'Ch34':(3,10),
                  'Ch11':(4,0),
                  'Ch10':(4,1),
                  'Ch09':(4,2),
                  'Ch08':(4,3),
                  'Ch07':(4,4),
                  'Ch29':(4,5),
                  'Ch28':(4,6),
                  'Ch27':(4,7),
                  'Ch26':(4,8),
                  'Ch25':(4,9),
                  'Ch24':(4,10),
                  'Ch12':(0,0),
                  'Ch59':(5,0),
                  'Ch13':(5,1),
                  'Ch14':(5,2),
                  'Ch15':(5,3),
                  'Ch16':(5,4),
                  'Ch17':(5,5),
                  'Ch18':(5,6),
                  'Ch19':(5,7),
                  'Ch20':(5,8),
                  'Ch21':(5,9),
                  'Ch22':(5,10)}

if __name__ == '__main__':
    
    ColTrs = {}
    for i in range(6):
        ColTrs[i] = [None] * 11
    for k, v in MCSMapFacingDown.iteritems():
        ColTrs[v[0]][v[1]] = k
        
        
    ##### 3rd session
    InFileM = '../Raw Data/2019-07-31T18-00-05B12784O18-T2-Longterm-Rec2.h5'
    InFileS = '../Raw Data/2019-07-31T18-00-05B12784O18-T2-Longterm-Rec2_2.h5'  
    CalFile = dd.io.load('../CalibrationFile/Cal-Gm20190731_033840.h5')
    DatFile = '../Matlab/2019-07-31T18-00-05B12784O18-T2-Longterm-Rec2'
    Bits = 16
    vgs = 0.25
    gmsign = -1
    Range = 20e-3 
    


    ivgainDC = pq.A/(118.8*pq.V) 
    ivgainAC = pq.A/(1188*pq.V) 
    Fhigh = 100 
    Flow = 0.001
    
    DCchs = ('ME5', 'ME7', 'ME29', 'ME31', 'SE5', 'SE7', 'SE29', 'SE31')
    ACchs = MCSMapFacingDown.keys()  
    Enc = ['M1','M2','S1','S2']
    
    Rec = ReadMCSFile(InFileM,
                      OutSeg=None,
                      SigNamePrefix='M')
    
    Rec = ReadMCSFile(InFileS,
                      OutSeg=Rec,
                      SigNamePrefix='S')
    
    
    plt.close('all')
    

#%%  Plot AC ############################################################ 
    plt.close('all')  
    FigSize = (25*0.394, 15*0.394)  
    Twind = (0*pq.s, None)
    VgsInVivo = CalFile['Vgs']
    CalRec = NeoSegment()
    tstop = np.array([])
    
    for sig in Rec.Signals():
        tstop = np.append(tstop,sig.t_stop)
    Twind = [0*pq.s, np.min(tstop)*pq.s]
    
    for isig, sig in enumerate(Rec.Signals()):
        if isig == 0:
            sig01 = sig.duplicate_with_new_array(np.zeros(len(sig.GetSignal(Time = Twind))))
            sig01.name = 'ME0'
            CalRec.AddSignal(sig01)
        if isig == 54:
            sig70 = sig.duplicate_with_new_array(np.zeros(len(sig.GetSignal(Time = Twind))))
            sig70.name = 'SE33'
            CalRec.AddSignal(sig70)   
        
        if sig.name in Enc:
            continue
        
        gm = gmsign * np.interp(vgs,VgsInVivo,CalFile['GM'][sig.name])*pq.A/pq.V

        if CalFile['ChKo'][sig.name]==True:
            gm = 1.0*pq.A/pq.V
        
        
        SigProAC = [{'function': RPro.Gain, 'args': {'Gain': ivgainAC}},
                    {'function': RPro.Gain, 'args': {'Gain': 1/gm}},
                    {'function': RPro.RemoveDC, 'args': {}},

                    ]
        
        SigProDC = [{'function': RPro.Gain, 'args': {'Gain': ivgainDC}},
                    {'function': RPro.Gain, 'args': {'Gain': 1/gm}},
                    {'function': RPro.SetZero, 'args': {'TWind': (None, 20*pq.s)}},
                    {'function': RPro.Filter, 'args': {'Type':'highpass',
                                              'Order':2,
                                              'Freqs':(Flow,)}}
                         ]
                        
        sig = Rec.GetSignal(sig.name)
        if sig.name in DCchs:
            sig.ProcessChain = SigProDC
        else:
            sig.ProcessChain = SigProAC

        sig = sig.GetSignal(Time = Twind)
    
        
        CalRec.AddSignal(sig)
    

    
    ChNames = {val:key for (key, val) in CalRec.SigNames.items()}
    
    SigProEnc = [{'function': RPro.Gain, 'args': {'Gain': 1.0/50}},
                    ]

    M1sig = Rec.GetSignal('M1')
    M2sig = Rec.GetSignal('M2')
    M1sig.ProcessChain = SigProEnc
    M2sig.ProcessChain = SigProEnc

    M1sig = M1sig.GetSignal(Time = Twind)
    M2sig = M2sig.GetSignal(Time = Twind)
    CalRec.AddSignal(M1sig)
    CalRec.AddSignal(M2sig)

    ChNames[66] = 'M1'
    ChNames[67] = 'M2'
    
    SortMap = np.ndarray([7, 11], dtype=object)
 
    for key, val in MCSMapFacingDown.items():
        SortMap[val[0], val[1]] = MCSMap[key]

    SortMap[6,0] ='M1'
    SortMap[6,1] ='M2'
    NeuroScopeMap = []       
    for col in SortMap:
        ColName = []
        ColCh = []
        for ch in col:
            if ch ==None:
                continue
            ColName.append(ch)
            ColCh.append(CalRec.SigNames[ch])
        NeuroScopeMap.append((ColName, ColCh))
    
    
    ExpDat = CalRec.ExportNeuroscope(DatFile, Range, Bits,'V', ChNames, NeuroScopeMap)
 

print('File exported to Neuroscope format, see "Cal Data" folder')